Ch. 3 Flashcards
Representativeness Heuristic
evaluating the probability of an uncertain event by the degree to which it is
- Similar in essential properties to its parent population
- Reflects the salient features of the process by which it is generated……
the ordering of events by their subjective probabilities coincides wit their ordering by representativeness (“does this seem like that?)
Representativeness
an assessment of the degree of correspondence between a sample and a population…….. an instance and a category, an act and an actor or, more generally, between an outcome and a model (e.g. judging by resemblance to a stereotype)………… e.g. “does this seem like that?”
Judging category membership
In what 2 ways is representativeness applied?
2 acts of substitution
Tom W
representativeness applied in 2 ways – 1. A prototype (representative exemplar) 2. Probability that the individual belongs to a category is judged by the degree of representativeness… …………..
categorical prediction by representativeness involves 2 acts of substitution – substitution of a prototypical exemplar for a category, and the substitution of the heuristic attribute of similarity for the target attribute of probability……………..
Following a person description (Tom W), the judged likelihood that he was in a particular specialisation correlated almost perfectly with ratings of how similar Tom W was to a typical student in each specialisation; participants appeared to neglect the base rates of students in those specialisations.
Category membership 2 criteria?
prototypes - (representative exemplars) appear to be used to represent categories…… similarity of the judged thing becomes probability
Base rates
the prevalences of particular categories in the environment
Prototype heuristic
the use of a prototype to represent a category… … also appear to be involved in other types of judgement, like economic evaluation (e.g. in the bird oil spill)…. Rely on a snapshot model
Peak-end effect (snapshot model) heuristic
What is it?
Why is it an instance of representativeness heuristic?
Dirty birds
colonoscopy
judge our past experiences almost entirely on how they were at their peak (pleasant or unpleasant) and how they ended. Other information is not lost, but it is not used. …………..
instance of representativeness heuristic because perceive not the sum of an experience but its average, e.g. only the most salient aspects are recalled
Sampling
people made large generalizations refardless of sample size, when they believed that the objects in question were homogenous in the population…….. also though people are aware that statistical reasoning makes sense, they often fail to apply it properly and instead apply the representativeness heuristic (e.g. hospital babies)
Law of large numbers
the mean value of a sample is more likely to fall within a specified bound of the parent population the larger that sample is…. …..
people show some intuitive understanding of this; willingness to generalize from a sample depends on beliefs bout variation in the sample/size of sample
Compound events
Linda the bank teller, see conjunction fallacy…….. combo of 2 events can’t be more probable than the prob of either event taken individually
Conjunction fallacy
What is it?
Linda the bankteller
how can the error be reduced?
estimating the conjunction to be more probable than either individual event; e.g. The Linda problem………
Linda bankteller – people are basing on representativeness rather than actual stats……….
Can reduce this error by providing cues to extensionality (all the events that are contributing to the probability), e.g. parcing them apart in steps
Gambler’s fallacy
people treat sequential outcomes of gambling as non-independent, e.g. that past outcomes affect the probs of future outcomes, though all outcomes are still equally likely
Hot-hand fallacy
false belief that player who just scored is more likely to score again
Availability heuristic
What is it?
Is it generally ecologically valid?
- estimates frequency or probability by the ease with which instances or associations could be brought to mind
- ecologically valid clue for judging frequency because more frequent events are easier to recall than infrequent ones
- can lead to biases, e.g. that recent or more familiar events will have bigger impact on judgment……. E.g. ppl overestimating their own contribution cause its easier to recall
- heuristic is generally ecologically valid!
Recall of content versus ease of retrieval
early studies didn’t distinguish between ease and number/frequency…… ease of recall shown to be main influence (Schwarz, ppl rating themselves more assertive after recalling less # of assertive examples)………
Support Theory
What does it distinguish between? What does it do?
How does implicitness/explicitness impact the judging of probability?
Distinguishes between events and hypothesis (decriptions of events)……. people judge the probability of hypotheses rather than events, considering support for hypotheses
Can be implicit (generalized category) or explicit (specific things or cases)…… but people often fail to unpack implicit hypotheses into explicit ones
Thus, support for implicits tends to be subadditive (the spport would be less if it were unpacked into components)
subadditivity
tendency to judge prob of the whole to be less than probs of the parts
prob of whole < parts
support for an implicit thing is less than it would be if the hypothesis was unpacked into components
People judging guilties neglected the support for the guilt of other suspects each time they judged an individual, rather it was related to the support
when more evidence introduced the probabilities for each suscpet increased further
Minerva DM
Multiple Trace Model?
What’s the process when making a judgment of likelihood under MDM?
How does it support availability heuristic?
integrates theories of judgment process with memory process
multiple-trace memory model - assumes that a new memory trace is created for each new event that is experienced (as ooposed to updating a memory trace)
memory probes are created to access memory, whereby “echoes” strong or weak are produced based on strength of similarity
When making a judgment of likelihood, MDM calculates an “echo intensity” that is based on the sum of the activation of all memory traces
has been used to explain availability phenomena
enhancement effect
enhancing subadditivity - increasing support for
superadditivity
Multiple-trace memory model
assumes a new trace is created for each event experienced (as opposed to updating a trace)
memory probe
activation of memory traces that produces an “echo” whose intensity is determined by the similarity to the prompt……..
MDM exaplining conjunction error, availability bias?
MM has been used to explain conjunction error - e.g. Linda - feminist vignette contains more details in the description, so that a memory probe for bank teller is weaker compared to feminist………… conjunctive probe for 2 events will return strong echoes from any memoery trace that has either A or B in it, thus exxagerating the echo
and availability - possibly strength of encoding ….. or inability to discriminate between events learned in different contexts, such that it is more salient in general
conditional probability
the probability of an event given that some other event has occurred
Bayes’ theorem can help us update beliefs in light of new evidence
Baye’s theorem
what do the different P( )s stand for?
P(H | D) =
P(D| H)×P(H)
P(D| H)×P(H)+P(D|~ H)×P(~ H)
P(H) = prior probability, probs that focal hypothesis is true prior to more info being obtained
P(D|H) = if hypothesis true, chacne of observing a certain outcome D
P(D|~H) = observing outcome D if the hypothesis is false
P(H|D) = posterior probability, probs that focal hypoythesis is true given the outcome D has been observed…… What we want to know!
conservatism
people insufficiently responsive to new evidence…. dont adjust enough……..
“book bag and poker chip” paradigm (blue or red chips)
however, variations in experimental paradigm seemed to change conservatism degree….. thus it may be just an artefact of experimental conditions
base rate neglect
and examples?
base rates don’t have the impact on judgements they should
e.g. Eddy/breast cancer, Gigerenzer HIV tests
Eddy/breast cancer conditional prob
Eddy - found that doctors over-relied on mammogram results when reasoning about a hypothertical breat caancer screening…. prior probabilities were neglected
Prob of mammogram detecting it: 79.3
Prob of cancer: 1
Prob of her having cancer after testing positive? 7.7%
how do you facilitate bayesian reasoning?
**Rewording problems in the form of frequencies, rather than single-event probabilities, can faciliatate bayesian reasoning(Gigerenzer/Hof)……. **
evolutionary reason for frequencies being dominant is that we encounter events in sequences, e.g. steps
- 10 out of 1000 women have b cancer
- 8out of every 10 women with b will get a positive mammogram
whats P(H) ?
P(H) = prior probability, probs that focal hypothesis is true prior to more info being obtained
What’s P(D|H) ?
P(D|H) = if hypothesis true, chacne of observing a certain outcome D
what’s P(D|~H) ?
P(D|~H) = observing outcome D if the hypothesis is false
what’s P(H|D)?
posterior probability
P(H|D) = posterior probability, probs that focal hypoythesis is true given the outcome D has been observed…… What we want to know!
bayesian reasoning phrased through frequencies - pro and con data?
Pro - correct answers ranged from 46% to 92% when using frequency, and frequency and visual aid compared to just 16% with standard phrasing (girginzer, Comsmides)
con - nested sets hypothesis - facilitation occurs when relationship btwn sets and subsets is made transparent (Macchi)
other evidence shows differences in motivation and prior ability influence the extent to which ppl get the right answer with frequency formats……… (Brase)
ppl at top universities outperformed others…… ppl paid outperformed those who werent
